July 2018
Intermediate to advanced
596 pages
13h 35m
Japanese
Content preview from Pythonによるデータ分析入門 第2版 ―NumPy、pandasを使ったデータ処理
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7.2
データの変形
219
0 pig
1 pig
2 pig
3 cow
4 cow
5 pig
6 cow
7 pig
8 salmon
Name: food, dtype: object
map
メソッドは、要素ごとの変換や、その他のデータクリーニング操作を行うのに便利な方法です。
7.2.3
値の置き換え
fillna
メソッドを用いた欠損値の穴埋めは、一般的な値の置き換え操作の特殊な例と考えることが
できます。また、先ほど紹介したように、
map
メソッドを用いると、オブジェクト内の一部の値を修正
できます。しかし、もっとシンプルで柔軟性が高いのは、
replace
を使う方法です。例えば次のような
シリーズについて考えてみましょう。
In [60]: data = pd.Series([1., -999., 2., -999., -1000., 3.])
In [61]: data
Out[61]:
0 1.0
1 -999.0
2 2.0
3 -999.0
4 -1000.0
5 3.0
dtype: float64
-999
という値はおそらく欠損値を示す標識でしょう。これらの値を、
pandas
が欠損値と理解できる
NA
に置き換えます。
replace
メソッドを用いると、（
inplace=True
を渡さない限りは）置き換えた新し
いシリーズが戻されます。
In [62]: data.replace(-999, np.nan)
Out[62]:
0 1.0
1 NaN
2 2.0
3 NaN
4 -1000.0
5 3.0
dtype: float64 ...
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